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Location Intelligence: Present and Future

It isn’t hard to see that location capable devices will become smaller, more mobile and more accurate in the coming years. With increased computing power and the introduction of quantum computing, location sharing will be safer, faster and easier to capture. Businesses will have far more location information at hand, and it will be up to AI integrated into the BI solution to provide the necessary insights.

By Nicholas Duggan

These days we hear a lot about the Fourth Industrial Revolution, which is characterized by the fusion of technologies and the blurring lines between physical, digital and biological worlds. Location Intelligence (LI) will be the underpinning technology that allows digital representations of real-world objects and gives business systems context. LI is a relatively new term to the geospatial industry, first appearing around 2012-2013, and is often used as part of a Business Intelligence (BI) solution, though you will see references to LI in all Geographic Information Systems (GIS).

What is Location Intelligence?

Strictly speaking, Location Intelligence uses location aspects of the many data collected as part of business working practice. In the last decade, businesses have started to realize that almost all the information they use has a geospatial aspect — Esri claims that approximately 80% of an organization’s data has a spatial component. This figure is not surprising when considering an average day at work. Just think about a simple business proposal. It is created for another company based somewhere else. The project or asset in there has a location, and so multiple location-based questions emerge: Has anyone else done a project nearby? Are there geographic risks as well as business risks? These questions can pose a potential risk to the project. LI provides vital insights to a business that can help in mitigating (geographic) risk, identifying areas of focus for new operations, and streamlining internal processes like travel and fleet management.

LI is the driving force behind not only where the shops should be built, but also the type of shop, demography, traffic, population and even the local weather. All of this data is analyzed when deciding on building a new supermarket

LI users

Retail
An obvious user of Location Intelligence will be the retail industry, especially supermarkets. LI is the driving force behind not only where the shops should be built, but also the type of shop, demography, traffic, population and even the local weather. All of this data is analyzed when deciding on building a new supermarket. It doesn’t just stop there. Have you ever considered how they figure out how much lettuce to order, or how to ensure that they aren’t overstocked on ice-cream? Of course, this is all linked into a LI solution. When the weather is nicer, more people eat salads and often have a barbeque. Therefore, they need more lettuce and ice-cream. Each store also has an Electronic Point of Sale (EPOS) system, so that when an item has its barcode scanned, not only does it tell the cashier how much you spent, but it also tells the BI system that the stock in the store is an item less — providing location-based data for the warehouse. The warehouse then gets the information about how much stock to buy, and which stores need replenishing. To comply with environmental laws and be as carbon neutral as possible, the most efficient delivery location routes are created and taken.

Healthcare
This is an industry that is starting to realize and flourish due to the uptake of Location Intelligence. It is hard to believe that hospitals and doctors have relied on clever IT systems and math to support some of the vital location analytics. In the United Kingdom, the National Health Service (NHS) has been given reduced funding year after year from the government and therefore, to be more intelligent with business cost, hospitals started using Business Intelligence systems. Although on the face of it healthcare doesn’t seem like the most obvious user of LI or GIS, but it is a sector that can benefit immensely from this technology.

After a patient has been transported to the hospital, it’s important that he/she is taken to the right place. Upon arrival, the paramedics are given the unit where the patient needs to go. This is a short distance as the hospital uses previous arrivals and indoor mapping analysis to understand the optimal place for such patients

Let us consider the journey of someone who has been injured in an accident and requires immediate medical attention. Having a system to find the shortest and quickest route to the person’s location is critical. So, the ability to get the best trained paramedics available for the type of injury the patient may have makes for an extremely valuable system that can save lives. All this is integrated into the BI system, which also includes the patients’ medical records, so that the first responder can take into consideration probable allergies to certain medication.

The healthcare sector is starting to flourish due to the uptake of Location Intelligence. It is hard to believe that hospitals and doctors have relied on clever IT systems and math to support some of the vital location analytics.

After the patient has been transported to the hospital, it’s important that he/she is taken to the right place. Upon arrival, the paramedics are given the unit where the patient needs to go. This is a short distance as the hospital has used previous arrivals and indoor mapping analysis to understand the optimal place for such patients. The BI system can then provide information about the correct blood type and the location of the nearest facility where that blood type is available. The blood is tagged and a digital checkout is done, so that its location can be tracked. At every stage of its journey, the temperature and status of the blood can be checked. The entire process has only taken 20 minutes, but it has all been recorded in the central system which ensures that people in each invested area of care are aware about what is happening.

Top Location Intelligence Use Cases

As the patient is taken for surgery, the BI system analyzes the best resources available and the time it will take for them to get there. This streamlines the staff and ensures quality of care. Any resource required is delivered to the operating room based on records of the injuries. The patient undergoes a successful surgery and is moved to the ward. But a few days after the discharge, he/she falls ill again and contacts the doctor, who records it on the BI system. It doesn’t take long for the system to notice that there are other patients with the same symptoms who were in the same ward at a similar time. On further inspection, LI can identify the location of the illness and its spread, and this information can be sent to the hospital to identify a potential risk for other patients, so that local services can allocate resources to the affected neighborhoods to aid in the reduction of spread.

This is just a single use case. In one day, there can be hundreds of admissions and emergency responses across hospitals, with each incident being an individual case, but analyzed as a whole to identify trends, hotspots and spread of a disease, either by standard algorithms or, as is more common now, through Artificial Intelligence (AI). By integrating BI systems with Location Intelligence, people can be saved and treated in a far more efficient and cost-effective manner.

Artificial intelligence will play a large part in how LI advances. This is because many of the business aspects can be modeled, and by applying more advanced AI, decisions which normally need manual intervention can be made automatically.

Future of Location Intelligence

Artificial intelligence will play a large part in how LI advances. This is because many of the business aspects can be modeled, and by applying more advanced AI, decisions which normally need manual intervention can be made automatically. Therefore, let’s first consider how AI will develop. At present, we are seeing the development and use of Artificial Narrow Intelligence (ANI), which is also called “weak AI”, as it can only focus on one area and maybe better than a human at that one focused task. An example of this is facial recognition. This is what most people have heard about, and it is a technology that is already becoming common. The next form of AI is Artificial General Intelligence (AGI), which covers multiple fields like power of reasoning, problem solving and abstract thinking, more like a real human mind with superpowers. We are just starting on this journey and do not expect to see this in a commercial form for another 25-30 years. By 2050, we will probably be in the realm that Stephen Hawking warned us about: Artificial Super Intelligence (ASI) — where AI surpasses humans in all areas.

Considering these types of AI is important as in the last five years, a new field of geospatial has appeared. It’s called GeoAI and it will play a major part in how BI and LI develop in the future. This falls into three areas — object detection, prediction and pattern analysis, all of which are vital to a business. As AI develops from a single task to multitasking and decision-making, it will be integrated more into business systems.

GeoAI will play a major part in how BI and LI develop in the future

Object detection
This is where objects can be identified from images, videos and even CCTV cameras. With a geospatial aspect, this can be used to identify the location of specific items or find out where certain people are going. A great LI application for this will be in real estate and using satellite imagery to identify new projects being started and their status over time. Mixed with AI super resolution (which turns blurred images into clear pictures), this can be a powerful tool. Construction sites are already starting to use this technology to identify workers not wearing full protective equipment and their location around the site. In future, all dangerous sites can monitor staff and their locations as well as equipment with centimeter precision straight to a map and the business system.

With a geospatial aspect, object detection can be used to identify the location of specific items or find out where certain people are going

Prediction
For many years, linear regression was the best way to predict future change in a geospatial context. Now it is very easy to hook in some AI prediction in python. Those implementing LI systems now will be the ones who will reap the rewards in future, as using temporal location data, such as competitor locations, debtor locations and won project locations can be used to see what issues can be around the corner.

Pattern detection
This will be another vital resource for LI in the future. Being able to crunch huge amounts of spatial information a company collates and then derive valuable insights, not only about operations but also customers and travel, is invaluable. As AI improves, it will be less onerous and more of an integrated part of the BI solution that gives an intelligent newsfeed.

Pattern detection will be another vital resource for LI in the future. Being able to crunch huge amounts of spatial information a company collates and then derive valuable insights, not only about operations but also customers and travel, is invaluable

It’s not just AI

AI won’t be the only change Location Intelligence will see. The recent breakthrough in technology from 7nm to 2nm chips is a ginormous development. Considering that only ten years ago we had 22nm chips and at the turn of the century we had 130nm chips, the move to 2nm is big. Think about the capability of mobile phones a decade ago and now, and then think about what that means over the next decade.

It isn’t hard to see that location capable devices will become smaller, more mobile and more accurate. With increased computing power and the introduction of quantum computing, location sharing will be safer, faster and easier to capture. Businesses will have far more location information at hand, in fact, too much for the average analytics team, and it will be up to AI integrated into the BI solution to provide the necessary insights.

In future, it will not be 80% of data that has a geospatial element; it will be in excess of 90% due to the learning curve business will undertake in realizing the value of geospatial.

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